Other experiments considered different data sources including characters (written on a tablet), and other features including physical properties such as speed and acceleration. In these tasks, other methods sometimes showed better performance, specifically a KNN classifier using edit distance with real penalties [1]} performed exceedingly well on the character classification tasks, and linear classifiers on the mode-of-transportation tasks.
We did not specifically design tasks to take advantage of the distances or embeddings that preserved the orientation of direction of the trajectories (as has been done elsewhere [2]}, e.g., by modifying the Pigeons dataset [3]}, [4]}, [5]}), and there was no significant observed advantage of that class of techniques over ones that simply treated trajectories as geometric objects. Identifying naturally occurring tasks where this is needed, and performing the empirical study is an intriguing future direction.
